import pandas as pd
import numpy as np
import matplotlib.pyplot as plt 

from skimage.io import imread, imshow
import cv2
from skfuzzy.cluster import cmeans
import itertools
import scipy

import warnings
warnings.filterwarnings('ignore')

image = imread('bacteries_1.jpg')
image1 = imread('bacteries_2.jpg')

imshow(image);

imshow(image1);

KMeans

def convert_to_2d(image):
    image_2d = image.reshape((-1,3)) 
    image_2d = np.float32(image_2d)
    
    return image_2d

image_2d = convert_to_2d(image)
image1_2d = convert_to_2d(image1)

MAX_ITER = 100
EPSILON = 0.85
CRITERIA = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, MAX_ITER, EPSILON)
ATTEMPTS = 10

CENTERS_METHODS = (cv2.KMEANS_PP_CENTERS, cv2.KMEANS_RANDOM_CENTERS)

CLUSTERS_RANGE = range(1, 11)

def calculate_intracluster_distance_sum(cluster_elements, centroid):
    return sum([scipy.spatial.distance.sqeuclidean(centroid, element) for element in cluster_elements])

def get_intercluster_distance(centroid, centers):
#     if len(centers) > 1:
#         return scipy.spatial.distance.pdist(centers, 'sqeuclidean').min()
    return sum([scipy.spatial.distance.sqeuclidean(centroid, centroid_other) for centroid_other in centers])
#     else:
#         return 0

def xie_benie(segmented_image, centers):
    xb = sum([calculate_intracluster_distance_sum(segmented_image[segmented_image == center], center)
              for center in centers]) / (
        sum([get_intercluster_distance(centroid, centers) for centroid in centers])
        * segmented_image.reshape((-1, 3)).shape[0])
    
    return xb

def kmeans_segmentation(image2d, original_image_shape, n_clusters, criteria, attempts, centers_method):

    retval, labels, centers = cv2.kmeans(image2d, n_clusters, None, criteria,
                                         attempts, centers_method) 

    centers = np.uint8(centers)
    segmented_data = centers[labels.flatten()] 
    segmented_image = segmented_data.reshape((original_image_shape))
    
    return segmented_image, centers

def create_segmented_mapping(image2d, original_image_shape, centers_methods, nclusters_range):
    mapping = {center: {n: None for n in nclusters_range} for center in centers_methods}
    
    for center_method, nclusters in itertools.product(centers_methods, nclusters_range):
        mapping[center_method][nclusters] = kmeans_segmentation(image2d, original_image_shape,
                                                                nclusters, criteria=CRITERIA,
                                                                attempts=ATTEMPTS,
                                                                centers_method=center_method)
    
    return mapping

segmented_images_kmeans_mapping = create_segmented_mapping(image_2d, image.shape, CENTERS_METHODS, CLUSTERS_RANGE)

segmented_images1_kmeans_mapping = create_segmented_mapping(image1_2d, image1.shape, CENTERS_METHODS,
                                                           CLUSTERS_RANGE)

fig, axs = plt.subplots(nrows=10, ncols=2, figsize=(20, 50))

for i, nclusters in enumerate(CLUSTERS_RANGE):
    for j, center_method in enumerate(CENTERS_METHODS):
        axs[i][j].imshow(segmented_images_kmeans_mapping.get(center_method).get(nclusters)[0], aspect='auto');
        axs[i][0].set_title('PP Centers, {} clusters'.format(nclusters))
        axs[i][1].set_title('Random Centers, {} clusters'.format(nclusters))

fig, axs = plt.subplots(nrows=10, ncols=2, figsize=(20, 50))

for i, nclusters in enumerate(CLUSTERS_RANGE):
    for j, center_method in enumerate(CENTERS_METHODS):
        axs[i][j].imshow(segmented_images1_kmeans_mapping.get(center_method).get(nclusters)[0], aspect='auto');
        axs[i][0].set_title('PP Centers, {} clusters'.format(nclusters))
        axs[i][1].set_title('Random Centers, {} clusters'.format(nclusters))

XIE-BENI

First Image

xie_beni_values = [[xie_benie(*segmented_images_kmeans_mapping.get(center_method).get(nclusters))
                    for nclusters in CLUSTERS_RANGE] for center_method in CENTERS_METHODS]

fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10, 6))

center_methods_titles = ('PP Centers', 'Random Centers')
for i in range(2):
    axs[i].plot(xie_beni_values[i])
    axs[i].set_title('Xie-Beni index, {} cluster centers'.format(center_methods_titles[i]))
    axs[i].set_xlabel('Number of cluster')

Second Image

xie_beni_values_image1 = [[xie_benie(*segmented_images1_kmeans_mapping.get(center_method).get(nclusters))
                    for nclusters in CLUSTERS_RANGE] for center_method in CENTERS_METHODS]

fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10, 6))

center_methods_titles = ('PP Centers', 'Random Centers')
for i in range(2):
    axs[i].plot(xie_beni_values_image1[i])
    axs[i].set_title('Xie-Beni index, {} cluster centers'.format(center_methods_titles[i]))
    axs[i].set_xlabel('Number of cluster')

Fuzzy C-Means

def fuzzy_cmeans_segmentation(image2d, original_shape, n_clusters, power, eps, max_iter):

    centers, fuzzy_matrix = cmeans(image2d.T, n_clusters, power, eps, max_iter, seed=42)[:2]
    centers = np.uint8(centers)
    
    segmented_image = centers[fuzzy_matrix.argmax(axis=0)]
    segmented_image = segmented_image.reshape(original_shape)
    
    return segmented_image, fuzzy_matrix, centers

M = 2

segmented_images_fuzzy = [fuzzy_cmeans_segmentation(image_2d, image.shape, nclusters, M, EPSILON, MAX_ITER)
                         for nclusters in CLUSTERS_RANGE]

fig, axs = plt.subplots(nrows=len(CLUSTERS_RANGE), ncols=1, figsize=(10, 50))

for i, nclusters in enumerate(CLUSTERS_RANGE):
    axs[i].imshow(segmented_images_fuzzy[i][0], aspect='auto');
    axs[i].set_title('Fuzzy C-Means, {} clusters'.format(nclusters))

segmented_images1_fuzzy = [fuzzy_cmeans_segmentation(image1_2d, image1.shape, nclusters, M, EPSILON, MAX_ITER)
                         for nclusters in CLUSTERS_RANGE]

fig, axs = plt.subplots(nrows=len(CLUSTERS_RANGE), ncols=1, figsize=(10, 50))

for i, nclusters in enumerate(CLUSTERS_RANGE):
    axs[i].imshow(segmented_images1_fuzzy[i][0], aspect='auto');
    axs[i].set_title('Fuzzy C-Means, {} clusters'.format(nclusters))

XIE-BENI

First Image

xie_beni_values_fuzzy_cmeans = [xie_benie(segmented_images_fuzzy[i][0], segmented_images_fuzzy[i][2]) 
                                for i, nclusters in enumerate(CLUSTERS_RANGE)]

plt.plot(xie_beni_values_fuzzy_cmeans)
plt.title('Xie-Beni index')
plt.xlabel('Number of cluster');

Second Image

xie_beni_values_fuzzy_cmeans_image1 = [xie_benie(segmented_images1_fuzzy[i][0], segmented_images_fuzzy[i][2]) 
                                for i, nclusters in enumerate(CLUSTERS_RANGE)]

plt.plot(xie_beni_values_fuzzy_cmeans_image1)
plt.title('Xie-Beni index')
plt.xlabel('Number of cluster');